1 code implementation • 19 Sep 2023 • Jianghao Wu, Guotai Wang, Ran Gu, Tao Lu, Yinan Chen, Wentao Zhu, Tom Vercauteren, Sébastien Ourselin, Shaoting Zhang
The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels.
1 code implementation • 22 Nov 2022 • Ran Gu, Guotai Wang, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Yinan Chen, Wenjun Liao, Shichuan Zhang, Kang Li, Dimitris N. Metaxas, Shaoting Zhang
First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image.
1 code implementation • 19 Aug 2022 • Guotai Wang, Xiangde Luo, Ran Gu, Shuojue Yang, Yijie Qu, Shuwei Zhai, Qianfei Zhao, Kang Li, Shaoting Zhang
Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost.
1 code implementation • 18 Aug 2022 • Ran Gu, Jingyang Zhang, Guotai Wang, Wenhui Lei, Tao Song, Xiaofan Zhang, Kang Li, Shaoting Zhang
To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain.
no code implementations • 14 Jun 2022 • Jingyang Zhang, Peng Xue, Ran Gu, Yuning Gu, Mianxin Liu, Yongsheng Pan, Zhiming Cui, Jiawei Huang, Lei Ma, Dinggang Shen
In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storage cost and privacy restriction.
1 code implementation • 13 May 2022 • Ran Gu, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Xiaofan Zhang, Guotai Wang, Shaoting Zhang
To tackle this deficiency, we propose Contrastive Domain Disentangle (CDD) network for generalizable medical image segmentation.
1 code implementation • 21 Nov 2021 • Wenhui Lei, Qi Su, Ran Gu, Na Wang, Xinglong Liu, Guotai Wang, Xiaofan Zhang, Shaoting Zhang
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.
1 code implementation • 18 Sep 2021 • Ran Gu, Jingyang Zhang, Rui Huang, Wenhui Lei, Guotai Wang, Shaoting Zhang
First, we present a domain composition method that represents one certain domain by a linear combination of a set of basis representations (i. e., a representation bank).
no code implementations • 6 May 2021 • Jingyang Zhang, Ran Gu, Guotai Wang, Hongzhi Xie, Lixu Gu
To solve this problem, we propose a Semi-Supervised Cross-Anatomy Domain Adaptation (SS-CADA) which requires only limited annotations for coronary arteries in XAs.
1 code implementation • 3 Feb 2021 • Wenhui Lei, Haochen Mei, Zhengwentai Sun, Shan Ye, Ran Gu, Huan Wang, Rui Huang, Shichuan Zhang, Shaoting Zhang, Guotai Wang
Despite the stateof-the-art performance achieved by Convolutional Neural Networks (CNNs) for automatic segmentation of OARs, existing methods do not provide uncertainty estimation of the segmentation results for treatment planning, and their accuracy is still limited by several factors, including the low contrast of soft tissues in CT, highly imbalanced sizes of OARs and large inter-slice spacing.
1 code implementation • 27 Jan 2021 • Haochen Mei, Wenhui Lei, Ran Gu, Shan Ye, Zhengwentai Sun, Shichuan Zhang, Guotai Wang
Delineation of Gross Target Volume (GTV) from medical images such as CT and MRI images is a prerequisite for radiotherapy.
no code implementations • 21 Jan 2021 • Ran Gu, Qiang Du, Simon J. L. Billinge
In the second stage, an interior point method is adopted to accelerate the local convergence.
Optimization and Control Numerical Analysis Numerical Analysis 65K10, 90C26 G.1.6; F.2.1
2 code implementations • 13 Dec 2020 • Wenhui Lei, Wei Xu, Ran Gu, Hao Fu, Shaoting Zhang, Guotai Wang
To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.
no code implementations • 7 Dec 2020 • Ran Gu, Gregory Gutin, Shasha Li, Yongtang Shi, Zhenyu Taoqiu
They also proved that every digraph on at most 6 vertices and arc-connectivity at least 2 has a good pair and gave an example of a 2-arc-strong digraph $D$ on 10 vertices with independence number 4 that has no good pair.
Combinatorics
1 code implementation • 22 Oct 2020 • Chia-Hao Liu, Christopher J. Wright, Ran Gu, Sasaank Bandi, Allison Wustrow, Paul K. Todd, Daniel O'Nolan, Michelle L. Beauvais, James R. Neilson, Peter J. Chupas, Karena W. Chapman, Simon J. L. Billinge
We validate the use of matrix factorization for the automatic identification of relevant components from atomic pair distribution function (PDF) data.
3 code implementations • 22 Sep 2020 • Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, Sébastien Ourselin, Tom Vercauteren, Shaoting Zhang
Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object.